Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models
Autor Sergios Theodoridisen Limba Engleză Paperback – 21 apr 2025
Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.
- Provides a number of case studies and applications on a variety of topics, such as target localization, channel equalization, image denoising, audio characterization, text authorship identification, visual tracking, change point detection, hyperspectral image unmixing, fMRI data analysis, machine translation, and text-to-image generation
- Most chapters include a number of computer exercises in both MatLab and Python, and the chapters dedicated to deep learning include exercises in PyTorch
New to this edition
- The new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models
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Specificații
ISBN-13: 9780443292385
ISBN-10: 0443292388
Pagini: 1200
Dimensiuni: 191 x 235 mm
Ediția:3
Editura: ELSEVIER SCIENCE
ISBN-10: 0443292388
Pagini: 1200
Dimensiuni: 191 x 235 mm
Ediția:3
Editura: ELSEVIER SCIENCE
Cuprins
1. Introduction
2. Probability and Stochastic Processes
3. Learning in Parametric Modelling: Basic Concepts and Directions
4. Mean-Square Error Linear Estimation
5. Stochastic Gradient Descent: the LMS Algorithm and its Family
6. The Least-Squares Family
7. Classification: A Tour of the Classics
8. Parameter Learning: A Convex Analytic Path
9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
10. Sparsity-Aware Learning: Algorithms and Applications
11. Learning in Reproducing Kernel Hilbert Spaces
12. Bayesian Learning: Inference and the EM Algorithm
13. Bayesian Learning: Approximate Inference and Nonparametric Models
14. Monte Carlo Methods
15. Probabilistic Graphical Models: Part 1
16. Probabilistic Graphical Models: Part 2
17. Particle Filtering
18. Neural Networks and Deep Learning: Part 1
19. Neural Networks and Deep Learning: Part 2
20. Dimensionality Reduction and Latent Variables Modeling
2. Probability and Stochastic Processes
3. Learning in Parametric Modelling: Basic Concepts and Directions
4. Mean-Square Error Linear Estimation
5. Stochastic Gradient Descent: the LMS Algorithm and its Family
6. The Least-Squares Family
7. Classification: A Tour of the Classics
8. Parameter Learning: A Convex Analytic Path
9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
10. Sparsity-Aware Learning: Algorithms and Applications
11. Learning in Reproducing Kernel Hilbert Spaces
12. Bayesian Learning: Inference and the EM Algorithm
13. Bayesian Learning: Approximate Inference and Nonparametric Models
14. Monte Carlo Methods
15. Probabilistic Graphical Models: Part 1
16. Probabilistic Graphical Models: Part 2
17. Particle Filtering
18. Neural Networks and Deep Learning: Part 1
19. Neural Networks and Deep Learning: Part 2
20. Dimensionality Reduction and Latent Variables Modeling
Recenzii
"This is a very complete Machine Learning book, as it covers statistical learning theory, both from frequentist and Bayesian perspectives. It also encompasses signal processing, probabilistic graphical models, deep learning, and latent variable modeling. It balances mathematical rigor with insightful comments to ease clear interpretation. The many examples make the text even more comprehensive. Each chapter has a well-curated list of references for further deepening on specific topics. Thus, it provides a thorough background for Machine Learning at an upper undergraduate level course. This book is also an excellent reference for practitioners to understand the necessary theory to apply Machine Learning with informed criteria." --Hamed Yazdanpanah, Postdoctoral Researcher, University of São Paulo
Reviews of the previous edition:
"Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars..." --Computing Reviews
"Machine Learning: A Bayesian and Optimization Perspective, Academic Press, 2105, by Sergios Theodoridis is a wonderful book, up to date and rich in detail. It covers a broad selection of topics ranging from classical regression and classification techniques to more recent ones including sparse modeling, convex optimization, Bayesian learning, graphical models and neural networks, giving it a very modern feel and making it highly relevant in the deep learning era. While other widely used machine learning textbooks tend to sacrifice clarity for elegance, Professor Theodoridis provides you with enough detail and insights to understand the "fine print". This makes the book indispensable for the active machine learner." --Prof. Lars Kai Hansen, DTU Compute - Dept. Applied Mathematics and Computer Science Technical University of Denmark
"Before the publication of Machine Learning: A Bayesian and Optimization Perspective, I had the opportunity to review one of the chapters in the book (on Monte Carlo methods). I have published actively in this area, and so I was curious how S. Theodoridis would write about it. I was utterly impressed. The chapter presented the material with an optimal mix of theoretical and practical contents in very clear manner and with information for a wide range of readers, from newcomers to more advanced readers. This raised my curiosity to read the rest of the book once it was published. I did it and my original impressions were further reinforced. S. Theodoridis has a great capability to disentangle the important from the unimportant and to make the most of the used space for writing. His text is rich with insights about the addressed topics that are not only helpful for novices but also for seasoned researchers. It goes without saying that my department adopted his book as a textbook in the course on machine learning." --Petar M. Djuric, Ph.D. SUNY Distinguished Professor Department of Electrical and Computer Engineering Stony Brook University, Stony Brook, USA
"As someone who has taught graduate courses in pattern recognition for over 35 years, I have always looked for a rigorous book that is current and appealing to students with widely varying backgrounds. The book on Machine Learning by Sergios Theodoridis has struck the perfect balance in explaining the key (traditional and new) concepts in machine learning in a way that can be appreciated by undergraduate and graduate students as well as practicing engineers and scientists. The chapters have been written in a self-consistent way, which will help instructors to assemble different sections of the book to suit the background of students" --Rama Cellappa, Distinguished University Professor, Minta Martin Professor of Engineering, Chair, Department of Electrical and Computer Engineering, University of Maryland, USA
Reviews of the previous edition:
"Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars..." --Computing Reviews
"Machine Learning: A Bayesian and Optimization Perspective, Academic Press, 2105, by Sergios Theodoridis is a wonderful book, up to date and rich in detail. It covers a broad selection of topics ranging from classical regression and classification techniques to more recent ones including sparse modeling, convex optimization, Bayesian learning, graphical models and neural networks, giving it a very modern feel and making it highly relevant in the deep learning era. While other widely used machine learning textbooks tend to sacrifice clarity for elegance, Professor Theodoridis provides you with enough detail and insights to understand the "fine print". This makes the book indispensable for the active machine learner." --Prof. Lars Kai Hansen, DTU Compute - Dept. Applied Mathematics and Computer Science Technical University of Denmark
"Before the publication of Machine Learning: A Bayesian and Optimization Perspective, I had the opportunity to review one of the chapters in the book (on Monte Carlo methods). I have published actively in this area, and so I was curious how S. Theodoridis would write about it. I was utterly impressed. The chapter presented the material with an optimal mix of theoretical and practical contents in very clear manner and with information for a wide range of readers, from newcomers to more advanced readers. This raised my curiosity to read the rest of the book once it was published. I did it and my original impressions were further reinforced. S. Theodoridis has a great capability to disentangle the important from the unimportant and to make the most of the used space for writing. His text is rich with insights about the addressed topics that are not only helpful for novices but also for seasoned researchers. It goes without saying that my department adopted his book as a textbook in the course on machine learning." --Petar M. Djuric, Ph.D. SUNY Distinguished Professor Department of Electrical and Computer Engineering Stony Brook University, Stony Brook, USA
"As someone who has taught graduate courses in pattern recognition for over 35 years, I have always looked for a rigorous book that is current and appealing to students with widely varying backgrounds. The book on Machine Learning by Sergios Theodoridis has struck the perfect balance in explaining the key (traditional and new) concepts in machine learning in a way that can be appreciated by undergraduate and graduate students as well as practicing engineers and scientists. The chapters have been written in a self-consistent way, which will help instructors to assemble different sections of the book to suit the background of students" --Rama Cellappa, Distinguished University Professor, Minta Martin Professor of Engineering, Chair, Department of Electrical and Computer Engineering, University of Maryland, USA